Contents:       Stata procedures for multiple imputation analysis.
Files:          miest.ado
                miest.hlp
                misum.ado
                misum.hlp
Version:        2.1
Date:           April 29, 2003
Author:         Kenneth Scheve
                Department of Political Science
                Yale University
                kenneth.scheve@yale.edu

DESCRIPTION

"miest" combines the results of statistical analyses across multiply imputed
datasets and reports the resulting multiple imputation estimates. "misum"
implements the same procedures but for calculating multiple imputation
estimates of the mean.

Multiple imputation is a method of statistical inference for incomplete
multivariate data. The method involves imputing m values for each
missing cell in your data matrix and creating m "completed" datasets.
Across these completed datasets, the observed values are the same, but the
missing values are filled in with different imputations that reflect 
uncertainty about the missing data. There are a number of alternative methods
for producing the imputations. One that is particularly simple to use is
Amelia: A Program for Missing Data by James Honaker, Anne Joseph, Gary King,
Kenneth Scheve, and Naunihal Singh. It is freely available as a standalone
windows program and as a Gauss program at http://GKing.Harvard.edu/.  After
creating the multiply imputed datasets, you then apply whatever statistical
method you would have used if there had been no missing values to each of
the m data sets, and use a simple procedure, implemented by "miest", to combine
the results. The multiple imputation point estimate for the parameters of
the statistical model is simply the average of the estimates across the m
analyses. The variance of this point estimate is the average of the estimated
variances from within each completed dataset, plus the sample variance in
the point estimates across the datasets (multiplied by a factor that corrects
for bias because the number of imputed datasets m is less than infinity). For
more information about multiple imputation, see "Analyzing Incomplete
Political Science Data: An Alternative Algorithm for Multiple Imputation"
American Political Science Review Vol. 95 No. 1 (March 2001):49-69
by Gary King, James Honaker, Anne Joseph and Kenneth Scheve.

Once you have installed the procedures, consult the on-line Stata help
files for a description of the command syntax for "miest" and "misum".

INSTALLATION

Copy miest.ado, miest.hlp, misum.ado, and misum.hlp to your personal ado
directory.  To get the name of this directory, type

    adopath

at the Stata command prompt.  You should see a message that looks like this:

   . adopath
     [1] (UPDATES)  "C:\STATA\ADO\UPDATES/"
     [2] (BASE)     "C:\STATA\ADO\BASE/"
     [3] (SITE)     "C:\STATA\ADO\SITE/"
     [4]            "."
     [5] (PERSONAL) "C:\ADO\PERSONAL/"
     [6] (STBPLUS)  "C:\ADO\STBPLUS/"
     [7] (OLDPLACE) "C:\ADO/"

The fifth directory is your personal ado directory; in this
example, it is C:\ADO\PERSONAL.  If this directory does not exist on your
computer, you will need to create the directory from a DOS prompt or by
using Windows Explorer.

ADDITIONAL INFORMATION

If you find a bug or have a comment about the procedure, please send mail to
the author at kenneth.scheve@yale.edu. Updates of this zip file will be
posted at http://GKing.Harvard.Edu.

Thanks to Nick Cox, James Honaker, Anne Joseph, Gary King, and Michael Tomz
for very helpful comments and suggestions.
